sPLINK (safe PLINK) allows the federated, privacy-preserving analysis of GWAS data. It works on distributed datasets without exchanging raw data and is robust against imbalanced phenotype distributions across cohorts. Federated and user-friendly analysis with sPLINK, thus, has the potential to replace meta-analysis as the gold standard for collaborative GWAS. The tool is available online [here](https://www.exbio.wzw.tum.de/splink/).
The EU H2020 project REPO-TRIAL aims at developing an _in silico_ approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrate heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome) and develop graph-based machine learning approaches to investigate this highly complex data.
The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.